How to Train a Text-to-Image Model with Fair AI Public License

Training a text-to-image model can seem daunting, especially with all the technical jargon and setup involved. However, with a structured approach, you can navigate this process smoothly. In this guide, we’ll simplify the steps to help you train your model using the Fair AI Public License 1.0 under the hood of the illustrious OnomaAI Research’s Illustrious-XL model.

Prerequisites

• Two Nvidia 3090 GPUs for optimal performance.
• The latest version of sd-scripts installed.
• A well-configured dataset – we recommend Arcaillous-XL.
• The appropriate licenses to use the dataset.

Setting Up Your Training Configuration

We will leverage a command line interface where you will trigger the training using various parameters. Think of this as setting the dials on a complex machine; each parameter controls a specific aspect of your training process:

NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 accelerate launch --num_cpu_threads_per_process 4 sdxl_train.py \
         --pretrained_model_name_or_path=aidatasdmodelsStable-diffusionIllustrious-XL-v0.1.safetensors \
         --dataset_config=arcaillous-xl.toml \
         --output_dir=resultsckpt --output_name=arcaillous-xl \
         --save_model_as=safetensors \
         --gradient_accumulation_steps 64 \
         --learning_rate=1e-5 --optimizer_type=Lion8bit \
         --lr_scheduler=constant_with_warmup --lr_warmup_steps 100 \
         --optimizer_args weight_decay=0.01 betas=0.9,0.95 \
         --min_snr_gamma 5 --sdpa --no_half_vae \
         --cache_latents --cache_latents_to_disk \
         --gradient_checkpointing \
         --full_bf16 --mixed_precision=bf16 \
         --save_precision=bf16 \
         --ddp_timeout=10000000 \
         --max_train_epochs 4 --save_every_n_epochs 1 \
         --save_every_n_steps 50

Parameter Breakdown: An Analogy

Imagine you are a chef preparing a complex dish. Each ingredient plays a crucial role in creating the final flavor. In the command above:

• NCCL_P2P_DISABLE and NCCL_IB_DISABLE: Think of these as your prep work – making sure your cooking environment is ready.
• –pretrained_model_name_or_path: This is your main ingredient (the core model) – a high-quality pre-prepared sauce.
• –dataset_config: Like your recipe, it guides how you’re combining your ingredients (data).
• –output_dir and –output_name: These are your serving plates – where the final dish will be presented.
• –learning_rate: Just like adjusting the oven temperature, it dictates how fast or slow flavors meld together (model learning).
• –max_train_epochs: This is the cooking time – the longer you let it cook, the better the flavors blend (the model learning from the dataset).

Common Troubleshooting Tips

While training your model, you may encounter a few bumps in the road. Here are some common issues and their resolutions:

• CUDA Out of Memory: If you run into memory errors, try reducing the batch size or gradient accumulation steps.
• Slow Training Time: Confirm that your GPUs are correctly utilized. You can check this using monitoring tools like nvidia-smi.
• Model Not Converging: Adjust the learning rate; sometimes minor tweaks can lead to better results.
• Save Errors: Ensure the output directory exists and has the appropriate permissions to save files.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

In summary, this structured approach to training your text-to-image model should empower you to tackle the complexities with confidence. As you proceed, remember that exploration and experimentation are key in the evolving world of AI.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.